diff --git a/comfy/model_sampling.py b/comfy/model_sampling.py
index e7f8bc6a..b46202b7 100644
--- a/comfy/model_sampling.py
+++ b/comfy/model_sampling.py
@@ -11,6 +11,14 @@ class EPS:
         sigma = sigma.view(sigma.shape[:1] + (1,) * (model_output.ndim - 1))
         return model_input - model_output * sigma
 
+    def noise_scaling(self, sigma, noise, latent_image, max_denoise=False):
+        if max_denoise:
+            noise = noise * torch.sqrt(1.0 + sigma ** 2.0)
+        else:
+            noise = noise * sigma
+        if latent_image is not None:
+            noise += latent_image
+        return noise
 
 class V_PREDICTION(EPS):
     def calculate_denoised(self, sigma, model_output, model_input):
diff --git a/comfy/samplers.py b/comfy/samplers.py
index e5569322..46d444f5 100644
--- a/comfy/samplers.py
+++ b/comfy/samplers.py
@@ -534,19 +534,13 @@ class KSAMPLER(Sampler):
         else:
             model_k.noise = noise
 
-        if self.max_denoise(model_wrap, sigmas):
-            noise = noise * torch.sqrt(1.0 + sigmas[0] ** 2.0)
-        else:
-            noise = noise * sigmas[0]
+        noise = model_wrap.inner_model.model_sampling.noise_scaling(sigmas[0], noise, latent_image, self.max_denoise(model_wrap, sigmas))
 
         k_callback = None
         total_steps = len(sigmas) - 1
         if callback is not None:
             k_callback = lambda x: callback(x["i"], x["denoised"], x["x"], total_steps)
 
-        if latent_image is not None:
-            noise += latent_image
-
         samples = self.sampler_function(model_k, noise, sigmas, extra_args=extra_args, callback=k_callback, disable=disable_pbar, **self.extra_options)
         return samples